Ordinal ranking and intensity of preference: A linear programming approach
Management Science
A faster algorithm for finding the minimum cut in a directed graph
SODA selected papers from the third annual ACM-SIAM symposium on Discrete algorithms
Fuzzy multiple criteria decision making: recent developments
Fuzzy Sets and Systems - Special issue on fuzzy multiple criteria decision making
An efficient algorithm for image segmentation, Markov random fields and related problems
Journal of the ACM (JACM)
The analytic hierarchy process: can wash criteria be ignored?
Computers and Operations Research
Solving the Convex Cost Integer Dual Network Flow Problem
Management Science
Linear programming models for estimating weights in the analytic hierarchy process
Computers and Operations Research
Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking
Management Science
An agent model based on ideas of concordance and discordance for group ranking problems
Decision Support Systems
Journal of Artificial Intelligence Research
A Hidden Pattern Discovery and Meta-synthesis of Preference Adjustment in Group Decision-Making
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Country credit-risk rating aggregation via the separation-deviation model
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm
Expert Systems with Applications: An International Journal
A spatial model for collaborative filtering of comments in an online discussion forum
Proceedings of the third ACM conference on Recommender systems
An approach to group ranking decisions in a dynamic environment
Decision Support Systems
Information Sciences: an International Journal
Using Gower Plots and Decision Balls to rank alternatives involving inconsistent preferences
Decision Support Systems
Rating Customers According to Their Promptness to Adopt New Products
Operations Research
Ranking sports teams and the inverse equal paths problem
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
A novel approach to probability distribution aggregation
Information Sciences: an International Journal
The k-allocation problem and its variants
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Synthesis ranking with critic resonance
Proceedings of the 3rd Annual ACM Web Science Conference
Mining consensus preference graphs from users' ranking data
Decision Support Systems
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
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The problem of group ranking, also known as rank aggregation, has been studied in contexts varying from sports, to multicriteria decision making, to machine learning, to ranking Web pages, and to behavioral issues. The dynamics of the group aggregation of individual decisions has been a subject of central importance in decision theory. We present here a new paradigm using an optimization framework that addresses major shortcomings that exist in current models of group ranking. Moreover, the framework provides a specific performance measure for the quality of the aggregate ranking as per its deviations from the individual decision-makers' rankings. The new model for the group-ranking problem presented here is based on rankings provided with intensity---that is, the degree of preference is quantified. The model allows for flexibility in decision protocols and can take into consideration imprecise beliefs, less than full confidence in some of the rankings, and differentiating between the expertise of the reviewers. Our approach relaxes frequently made assumptions of: certain beliefs in pairwise rankings; homogeneity implying equal expertise of all decision makers with respect to all evaluations; and full list requirement according to which each decision maker evaluates and ranks all objects. The option of preserving the ranks in certain subsets is also addressed in the model here. Significantly, our model is a natural extension and generalization of existing models, yet it is solvable in polynomial time. The group-rankings models are linked to network flow techniques.